The Hadoop ecosystem is an eclectic mishmash of start-ups, mid-sized vendors and IT heavyweights with products and services up and down the Big Data stack. Inevitably the ecosystem will consolidate and thin itself out through mergers, acquisitions and – unfortunately for some of these start-ups – bankruptcies.
Consolidation is part of the natural evolution of any given technology market after an initial period of frenzied innovation, and the Big Data market is no exception. I believe we are witnessing the start of this consolidation today. It will take several years to play out, but the first phase of consolidation is manifesting itself in the form of strategic technical partnerships between vendors that play in different segments of the Hadoop market.
theCUBE’s next stop is the Hilton Santa Clara for #BigDataSV. The show takes place February 11-13, 2014 and you can catch all the action live at SiliconANGLE.tv. We’ll have analysis of all the news breaking at Strata Conference, as well as in-depth conversations with leading Big Data and Data Science thought-leaders.
In addition to the broadcast, we’re also throwing a little soiree the evening of February 12 at the Hilton. If you’re attending Strata, please joins us between 6pm-8pm PST in the Coastal Room for some drinks and hors d’oeuvres and socializing. RSVP here.
IBM’s annual revenue last year dropped below $100 billion for the first time since 2010. The company’s fourth quarter results were particularly weak, coming in 5.5% below expectations. This was due in large part to IBM’s struggling hardware business, with revenue dropping a staggering 27%.
I’ve already laid out my predictions for Big Data in 2014, but I also wanted to let the Wikibon community know how my colleagues and I plan to cover Big Data in the year ahead. We’ve organized our research agenda into three major buckets.
Technology. Clearly the technologies and products that collectively make up Big Data – including Hadoop, NoSQL data stores, analytic databases, data visualization tools and more – are maturing at a rapid pace (much faster, for example, than relational databases did in the 1980s.) Big Data is also applicable across industries, meaning these technologies are inevitably and increasingly intersecting with adjacent technology movements, namely the cloud, mobile computing and social media. As we have for the last several years, Wikibon will devote significant coverage to these developments with an eye on putting technology innovations in context for enterprise Big Data practitioners (both technology practitioners and line-of-business practitioners.)
The salary/benefits package is substantially lower than you need
You might be thinking that this reason is too obvious to make a list like this. However, too often, I hear stories from people who have accepted positions below their salary expectations only to eventually become resentful and looking for a way to correct the error. As long as your salary and benefit expectations are reasonable for:
Regardless of organization vertical or size, security has been and will continue to be an incredibly important part of the risk management portfolio. It’s how security is handled that will determine the overall effectiveness of chief security office position, though.
The security spectrum
Security is generally seen as a spectrum. At one end of the spectrum is the wild west kind of environment. In the wild west, anything goes and security is an afterthought. In such environments, there is generally no security officer and every employee just does what they want when they want it. If there is any security, it’s left up to the individual. In these environments, employees can always get their job done thanks to the lack of red tape, but there is a high risk of downtime and data compromise.
Today, I read an article at SearchVirtualStorage entitled SDS a fancy way to say virtualization, says DataCore Software chairman. In this article, DataCore Software Corp. chairman and founder Ziya Aral indicates that his belief is that SDS is essentially just virtualization and that he doesn’t really see the difference between the two terms.
SDS is a superset that includes virtualization
First, I understand where Mr. Aral is coming from as there are major similarities between the two concepts and people use the terms interchangeably and, let’s face it – vendors love to invent new terms all the time in a valiant effort to prove their forward-thinkingness and we’re seeing software-defined everything these days. However, I see SDS is a superset technology that includes virtualization as one of its primary components.
See videos and articles from Big Data NYC 2013 event.
It’s an exciting week for us here at SiliconANGLE Wikibon. Today we are kicking off our #BigDataNYC event on #theCUBE live from New York City. We will be covering all the Big Data-related action taking place in the city this week, including the goings on at the Strata + Hadoop World conference and other events.
- Panic and hysteria
- Search for the guilty
- Punishment of the innocent
- Praise and honor for the nonparticipants
Unfortunately, while formal professional project managers would certainly hope to avoid projects that go like this, many project do take this calamitous route. Here, I’m going to explain the thinking that results in these kinds of issues and provide some tips for avoiding certain project doom.